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in analysis of single-cell and spatial data. Key Responsibilities Collaborate with PhD students, postdoctoral researchers and other technicians. Analyze and integrate single-cell and spatial data
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-edge technologies for next generation sequencing. Our user base consists of both VIB and non-VIB scientists throughout Europe. Key applications are gene expression analysis, whole genome sequencing
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colleagues on multi‑omics data integration and analysis. You will also work with AI experts to help implement predictive models that improve guide design and functional genomics workflows. You will join an
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and propose meaningful and testable hypotheses, grounded in disease biology. Perform end‑to‑end processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g
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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
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cytometry, including the design, optimization, and analysis of complex high-parameter panels. You will take a leading role in driving multiple projects and serve as a core resource for the team by supporting
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open-source software packages and set up computational pipelines. As a bio-informatics research lab, we publish software packages in Python and R for specific, cutting-edge analysis of large-scale
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Biology (VIB.AI), more specifically in the Ghent branch of VIB.AI. Profile text Academic education You contribute to various lecturers in the discipline of bioinformatics, data analysis of large-scale (bio
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the discipline of bioinformatics, data analysis of large-scale (bio)medical data, applications of artificial intelligence and machine learning. You contribute to high-quality teaching in bachelor and master years
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Maintaining consumables stocks necessary for the above two activities Basic maintenance of peptide synthesizer, LCMS and HPLC Biophysical analysis of proteins Documentation of experimental